Paper Citation Record · LEDGER

SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2308.15030.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2308.15030 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:05:46.931117Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T16:36:06.598162Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c0b83b85-3b3a-443d-a989-957341a6846b · inbound

Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference cites this paper.

Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:05:46.931117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a696b95e-0a83-4776-b663-616130dc9387 · inbound

A Survey on Inference Optimization Techniques for Mixture of Experts Models cites this paper.

A Survey on Inference Optimization Techniques for Mixture of Experts Models SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.676859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 019e539f-2c11-4e2e-9ee7-ebebf948bffa · inbound

EcoServe: Designing Carbon-Aware AI Inference Systems cites this paper.

EcoServe: Designing Carbon-Aware AI Inference Systems SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T20:31:35.704164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c42b0a87-959e-45bf-aacd-6126a805075a · inbound

Efficient Deployment of Vision-Language Models on Mobile Devices: A Case Study on OnePlus 13R cites this paper.

Efficient Deployment of Vision-Language Models on Mobile Devices: A Case Study on OnePlus 13R SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:20:53.562080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b39b1e94-6c3d-4c17-9851-c95924dd86fc · inbound

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? cites this paper.

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T21:51:05.092526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf7cb693-3216-476d-aae0-c18d55c558fc · inbound

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs cites this paper.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.891720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8bc58f2-7627-41e6-81e9-ee57e0289148 · inbound

VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading cites this paper.

VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:06.601343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.